Chihaya Koriyama August 17 (Lecture 1) Case-control study Chihaya Koriyama August 17 (Lecture 1)

Study design in epidemiology Observational study individual Case-control study Cohort study population Ecological intervention

Why case-control study? In a cohort study, you need a large number of the subjects to obtain a sufficient number of case, especially if you are interested in a rare disease. Gastric cancer incidence in Japanese male: 128.5 / 100,000 person year A case-control study is more efficient in terms of study operation, time, and cost.

Case-control study - subjects Start with identifying the cases of your research interest. If you can identify the cases systematically, such as a cancer registration, that would be better. Incident cases (newly diagnosed cases) are better than prevalent cases (=survivors). Recruitment of appropriate controls From residents, patients with other disease(s), cohort members who do not develop the disease yet.

Various types of case-control studies １）a population-based case-control study Both cases and controls are recruited from the population. ２）a case-control study nested in a cohort Both case and controls are members of the cohort. ３）a hospital-based case-control study Both case and controls are patients who are hospitalized or outpatients.

Who will be controls? Control ≠ non-case Controls are also at risk of the disease in his(her) future. In a case-control study of gastric cancer, a person who has received the gastrectomy cannot be a control. In a case-control study of car accident, a person who does not drive a car cannot be a control.

Case-control study - information Collection of the information (past information) by interview, biomarkers, or medical records Exposure (your main interest) Potential confounding factors Bias & Confounding Selection bias Information bias (recall bias) confounding

Selection bias Sampling is required in a case-control study (since we cannot examine all!) We need to chose appropriate subjects. Selection bias is “Selection of cases and controls in a way that is related to exposure leads to distortions of exposure prevalence”.

Error & Bias Error: random error Bias：systematic error differential misclassification non-differential misclassification This is a problem!

An example of non-differential misclassification in an exposure variable We want to compare mean of blood pressure levels between cases and controls. The blood pressure checker has a problem and always gives 5mmHg-higher than true values. All subjects were examined by the same blood pressure checker. →　no problem for internal comparison

Observed risk estimate always comes close to “1(null)” An example of non-differential misclassification in the ascertainment of exposure Case Control Odds ratio True (nobody knows) Exp + 50 Exp - 10 90 Results of test＊ 41 49 19 91 1 10 9 10 (50*90) / (50*10) =9 (41*91) / (49*19)=4.01 ＊Sensitivity 80% (80% of the exposed subjects are correctly diagnosed) Specificity 90% (90% of the un-exposed subjects are correctly diagnosed)

Differential misclassification Selection bias Detection bias Information bias Recall bias Family information bias

Confounding Confounders are risk factors for the outcome. Confounders are related to exposure of your interest. Confounders are NOT in the process of causal relationship between the exposure and the outcome of your interest.

Example of confounder - living in a HBRA is a confounder - HBRA: high background radiation area Low socio-economical status in HBRA A surrogate marker of low socio-economic status High infant death Living in a HBRA Causation ? Exposure to radiation in uterus

Example of confounder - smoking is a confounder - Smoking is a risk factor of MI Myocardial infarction Causation ? (We observe an association) smoking Radiation related by chance

Example of “not” confounder - pineal hormone is not a confounder - EMF: electro-magnetic field Decrease of pineal hormone may be the risk of breast ca. Breast cancer Down regulation of pineal hormone Causation ? 電磁場の乳がんに対する影響が全て松果体ホルモン低下で説明できるなら、松果体ホルモンレベルは交絡因子として取り扱うべきでない EMF EMF exposure induces down regulation of pineal hormone If EMF exposure cause breast cancer only through down regulation of pineal hormone, this is not a confounder.

Why do we have to consider confounding? We want to know the “real” causal association but a distorted relationship remains if you do not adjust for the effects of confounding factors.

How can we solve the problem of confounding? “Prevention” at study design Limitation Randomization in an intervention study Matching in a cohort study But not in a case-control study

How can we solve the problem of confounding? “Treatment “ at statistical analysis Stratification by a confounder Multivariate analysis

Case ascertainment Who is your case? Patient? Deceased person? What is the definition of the case? Cancer (clinically? Pathologically?) Virus carriers (Asymptomatic patients) → You need to screen the antibody

Incident or Prevalent cases with chronic disease(s) Incident case Prevalent case You recruit cases prospectively. Newly diagnosed cases All cases are alive. You recruit cases cross-sectionaly. Mixed cases with diagnosed recently and long time ago. You miss patients who died before study. Only survivors Cases with better prognosis!

Matching in a case-control study Matched by confounding factor(s) Sex, age ・・・・ Cannot control confounding Conditional logistic analysis is required. To increase the efficiency of statistical analysis

Over matching Matched by factor(s) strongly related to the exposure which is your main interest CANNOT see the difference in the exposure status between cases and controls

↓ ↓ a case-control study Cases Controls (brain tumor) N=100 N=100 Mobile phone users (NOT recently started) ↓ ↓ 50 10  The incubation period of tumor is a few years at least. Cases Controls Yes 50 10 No 90

Risk measure in a case-control study Odds = prevalence / (1－ prevalence) Odds ratio = odds in cases / odds in controls Disease +（case） －（control） + a c Exposure － b d Exposure odds in cases ＝a / b Exposure odds in controls＝c / d Odds ratio＝(a / b) / (c / d) ＝ a * d / b * c

Comparison of the study design Case-control Cohort Rare diseases suitable not suitable Number of disease 1 1< Sample size relatively small need to be large Control selection difficult easier Study period relatively short long Recall bias yes no Risk difference no available available